.ipynb .pdf

Binder

Contents

X-Band PPI Analysis

During this same analysis period, there was an X-Band radar operated by CSU scanning in both PPI/RHI scan modes

Imports

import pyart
import glob
import xarray as xr
import matplotlib.pyplot as plt
import cartopy.crs as ccrs
import numpy as np
from math import atan2 as atan2
import warnings
import xcollection as xc
import hvplot.xarray
import holoviews as hv
warnings.filterwarnings("ignore")
hv.extension('bokeh')

Data Overview

The data were accessed from the ARM data portal - here is a link to the specific query, with our date being March 14, 2022

We will be looking at the 6 degree elevation scan, since there is better data coverage in the clouds/less beam blockage due to terrain

Analysis Workflow

Read in the Data

We grab the files from the following directory, after ordering from the data portal and unzipping the archive

radar_files = sorted(glob.glob('../../data/x-band/ppi/*6_PPI.nc'))

Setup Helper Functions

We include some helper functions to build the scale bar for the plots

def gc_latlon_bear_dist(lat1, lon1, bear, dist):
    """
    Input lat1/lon1 as decimal degrees, as well as bearing and distance from
    the coordinate. Returns lat2/lon2 of final destination. Cannot be
    vectorized due to atan2.
    """
    re = 6371.1  # km
    lat1r = np.deg2rad(lat1)
    lon1r = np.deg2rad(lon1)
    bearr = np.deg2rad(bear)
    lat2r = np.arcsin((np.sin(lat1r) * np.cos(dist/re)) +
                      (np.cos(lat1r) * np.sin(dist/re) * np.cos(bearr)))
    lon2r = lon1r + atan2(np.sin(bearr) * np.sin(dist/re) *
                          np.cos(lat1r), np.cos(dist/re) - np.sin(lat1r) *
                          np.sin(lat2r))
    return np.rad2deg(lat2r), np.rad2deg(lon2r)
 
    
def add_scale_line(scale, ax, projection, color='k',
                  linewidth=None, fontsize=None, fontweight=None):
    """
    Adds a line that shows the map scale in km. The line will automatically
    scale based on zoom level of the map. Works with cartopy.
 
    Parameters
    ----------
    scale : scalar
        Length of line to draw, in km.
    ax : matplotlib.pyplot.Axes instance
        Axes instance to draw line on. It is assumed that this was created
        with a map projection.
    projection : cartopy.crs projection
        Cartopy projection being used in the plot.
 
    Other Parameters
    ----------------
    color : str
        Color of line and text to draw. Default is black.
    """
    frac_lat = 0.1  # distance fraction from bottom of plot
    frac_lon = 0.5  # distance fraction from left of plot
    e1 = ax.get_extent()
    center_lon = e1[0] + frac_lon * (e1[1] - e1[0])
    center_lat = e1[2] + frac_lat * (e1[3] - e1[2])
    # Main line
    lat1, lon1 = gc_latlon_bear_dist(
        center_lat, center_lon, -90, scale / 2.0)  # left point
    lat2, lon2 = gc_latlon_bear_dist(
        center_lat, center_lon, 90, scale / 2.0)  # right point
    if lon1 <= e1[0] or lon2 >= e1[1]:
        warnings.warn('Scale line longer than extent of plot! ' +
                      'Try shortening for best effect.')
    ax.plot([lon1, lon2], [lat1, lat2], linestyle='-',
            color=color, transform=projection, 
            linewidth=linewidth)
    # Draw a vertical hash on the left edge
    lat1a, lon1a = gc_latlon_bear_dist(
        lat1, lon1, 180, frac_lon * scale / 20.0)  # bottom left hash
    lat1b, lon1b = gc_latlon_bear_dist(
        lat1, lon1, 0, frac_lon * scale / 20.0)  # top left hash
    ax.plot([lon1a, lon1b], [lat1a, lat1b], linestyle='-',
            color=color, transform=projection, linewidth=linewidth)
    # Draw a vertical hash on the right edge
    lat2a, lon2a = gc_latlon_bear_dist(
        lat2, lon2, 180, frac_lon * scale / 20.0)  # bottom right hash
    lat2b, lon2b = gc_latlon_bear_dist(
        lat2, lon2, 0, frac_lon * scale / 20.0)  # top right hash
    ax.plot([lon2a, lon2b], [lat2a, lat2b], linestyle='-',
            color=color, transform=projection, linewidth=linewidth)
    # Draw scale label
    ax.text(center_lon, center_lat - frac_lat * (e1[3] - e1[2]) / 4.0,
            str(int(scale)) + ' km', horizontalalignment='center',
            verticalalignment='center', color=color, fontweight=fontweight,
            fontsize=fontsize)

Configure our Main Plotting Function

We use the RadarMapDisplay from PyART as the main plotting utility, with a few customizations

def plot_ppi(file, centerlon, centerlat, other_field='ZDR'):
    
    # Read in the data using pyart
    radar = pyart.io.read_cfradial(file)
    
    # Quickly qc data using velocity texture
    vel_texture = pyart.retrieve.calculate_velocity_texture(radar, vel_field='VEL', wind_size=3, nyq=33.4)
    radar.add_field('velocity_texture', vel_texture, replace_existing=True)
    
    # Add a gatefilter, and filter using a threshold value of 4 - could be refined
    gatefilter = pyart.filters.GateFilter(radar)
    gatefilter.exclude_above('velocity_texture', 4)
    
    # Setup our figure
    fig = plt.figure(figsize=(30, 10))
    
    #delta lat lon degrees
    window = 0.2
    locbox = [centerlon - window, centerlon + window, centerlat - window, centerlat + window]
    
    # create the plot
    ax1 = fig.add_subplot(121, projection=ccrs.PlateCarree())
    
    # Setup a RadarMapDisplay, which gives our axes in lat/lon
    display = pyart.graph.RadarMapDisplay(radar)
    
    # Add our reflectivity (DBZ) field to the plot, including our gatefilter
    display.plot_ppi_map('DBZ', 0, ax=ax1, vmin=-20, vmax=40.,
                         gatefilter=gatefilter)
    
    # Set our bounds
    plt.xlim(locbox[0], locbox[1])
    plt.ylim(locbox[2], locbox[3])
    
    # Label where the KAZR radar is at and place the legend in the upper right
    ax1.scatter(centerlon, centerlat, marker='o', s=100, color='k',
                label='KAZR Radar')
    plt.legend(loc='upper right')
    
    # Add our scale bar
    add_scale_line(10.0, ax1, projection=ccrs.PlateCarree(), 
                   color='black', linewidth=3,
                   fontsize=20,
                   fontweight='bold')
    
    # Part 2 - plot either ZDR or PHIDP
    ax2 = fig.add_subplot(122, projection=ccrs.PlateCarree())
    
    if other_field == 'ZDR':
        
        # Plot ZDR, using the bounds discussed on Slack - notice the negative values
        display.plot_ppi_map('ZDR', 0, ax=ax2, vmin=-1.5, vmax=0.,
                             gatefilter=gatefilter,
                             cmap='pyart_Carbone42')
        
    elif other_field == 'PHIDP':
    
        display.plot_ppi_map('PHIDP', 0, ax=ax2,
                             vmin=90, vmax=120,
                             gatefilter=gatefilter,
                             cmap='pyart_Carbone42')
    
    # Adjust the bounds
    plt.xlim(locbox[0], locbox[1])
    plt.ylim(locbox[2], locbox[3])
    
    # Add a point for the KAZR radar
    ax2.scatter(centerlon, centerlat, marker='o', s=100, color='k',
                label='KAZR Radar')
    plt.legend(loc='upper right')
    
    # Add a scale bar
    add_scale_line(10.0, ax2, projection=ccrs.PlateCarree(), 
                   color='black', linewidth=3,
                   fontsize=20,
                   fontweight='bold')
    
    # Create the image path from the time
    image_path = f"../images/xband_ppi/xband_ppi_{radar.time['units'][-8:].replace(':', '.')}.png"

    # Make sure it has a white background - helps with dark mode in slack
    plt.savefig(image_path, facecolor='white', transparent=False)
    
    # Show the figure, then close!
    plt.show()
    plt.close()

Test the Plotting on a Single File

Let’s test out this function on a single file…

We also need to read in the lat/lon of the KAZR radar

kazr_ds = xr.open_dataset('../../data/ka-band/guckazrcfrgeM1.a1.20220314.000003.nc')

We can grab the latitude and longitude from this dataset…

lat = kazr_ds.latitude.values
lon = kazr_ds.longitude.values
print(f'{lat}, {lon}')

38.95615768432617, -106.98785400390625

plot_ppi(file=radar_files[0],
         centerlat=lat,
         centerlon=lon)

Final Data Visualization

Let’s plot all of our files!

for file in radar_files:
    plot_ppi(file, centerlat=lat, centerlon=lon)

Use hvPlot to looking at PPIs

---

def create_dataset_from_sweep(radar_file, sweep=0, field=None):
    """
    Creates an xarray.Dataset from sweeps
    """
    
    # Grab the range
    range = radar.range['data']
    
    # Grab the elevation
    elevation = radar.get_elevation(sweep)
    
    # Grab the azimuth
    azimuth = radar.get_azimuth(sweep)
    
    # Grab the x, y, z values
    x, y, z = radar.get_gate_x_y_z(sweep)
    
    # Grab the lat, lon, and elevation
    lat, lon, alt = radar.get_gate_lat_lon_alt(sweep)
    
    # Add the fields
    field_dict = {}
    for field in list(radar.fields):
        field_dict[field]=(["azimuth", "range"], radar.get_field(sweep, field))
    
    ds = xr.Dataset(
        data_vars=field_dict,
         coords=dict(
             x=(["azimuth", "range"], x),
             y=(["azimuth", "range"], y),
             z=(["azimuth", "range"], z),
             lat=(["azimuth", "range"], lat),
             lon=(["azimuth", "range"], lon),
             alt=(["azimuth", "range"], alt),
             azimuth=azimuth,
             elevation=elevation,
             sweep=np.array([sweep]),
             range=range),
    )
    
    return ds.chunk()

def convert_to_xradar(radar):
    """
    Converts from radar to xradar
    """
    ds_list = []
    sweeps = radar.sweep_number['data']
    for sweep in sweeps:
        ds_list.append(create_dataset_from_sweep(radar, sweep))
    
    # Convert the numpy array to a list of strings
    dict_keys = [x for x in list(sweeps.astype(str))]
    
    # Zip the keys and datasets and turn into a dictionary
    dict_of_dsets = dict(zip(dict_keys, ds_list))
    
    return xc.Collection(dict_of_dsets)

ds_list = []

for radar_file in radar_files:
    ds_list.append(convert_to_xradar(pyart.io.read(radar_file))['0'])

ds = xr.concat(ds_list, dim='time')

ds[list(ds.coords)].load()

<xarray.Dataset>
Dimensions:    (azimuth: 1157, range: 675, elevation: 1157, sweep: 1)
Coordinates:
    x          (azimuth, range) float32 -0.2452 -0.1151 0.01506 ... 143.9 144.1
    y          (azimuth, range) float32 -112.3 -52.72 ... 3.999e+04 4.005e+04
    z          (azimuth, range) float32 -12.0 -6.0 0.0 ... 4.29e+03 4.296e+03
    lat        (azimuth, range) float32 38.9 38.9 38.9 ... 39.26 39.26 39.26
    lon        (azimuth, range) float32 -106.9 -106.9 -106.9 ... -106.9 -106.9
    alt        (azimuth, range) float64 3.137e+03 3.143e+03 ... 7.445e+03
  * azimuth    (azimuth) float32 0.1251 0.1251 0.3215 ... 0.2062 0.2062 0.2062
  * elevation  (elevation) float32 5.933 5.944 5.949 5.949 ... 5.988 5.988 5.988
  * sweep      (sweep) int32 0
  * range      (range) float32 -112.9 -53.0 6.937 ... 4.023e+04 4.029e+04
Data variables:
    *empty*

xarray.Dataset

• Dimensions:
  • azimuth: 1157
  • range: 675
  • elevation: 1157
  • sweep: 1
• Coordinates: (10)
  • x
    (azimuth, range)
    float32
    -0.2452 -0.1151 ... 143.9 144.1

    array([[-2.4522302e-01, -1.1508123e-01,  1.5060368e-02, ...,
             8.7166641e+01,  8.7296654e+01,  8.7426666e+01],
           [-2.4521814e-01, -1.1507894e-01,  1.5060067e-02, ...,
             8.7164818e+01,  8.7294838e+01,  8.7424835e+01],
           [-6.3044101e-01, -2.9586101e-01,  3.8718525e-02, ...,
             2.2409538e+02,  2.2442966e+02,  2.2476392e+02],
           ...,
           [-4.0424091e-01, -1.8970707e-01,  2.4826448e-02, ...,
             1.4369028e+02,  1.4390459e+02,  1.4411891e+02],
           [-4.0424091e-01, -1.8970707e-01,  2.4826448e-02, ...,
             1.4369028e+02,  1.4390459e+02,  1.4411891e+02],
           [-4.0424091e-01, -1.8970707e-01,  2.4826448e-02, ...,
             1.4369028e+02,  1.4390459e+02,  1.4411891e+02]], dtype=float32)

  • y
    (azimuth, range)
    float32
    -112.3 -52.72 ... 4.005e+04

    array([[-1.12340324e+02, -5.27204285e+01,  6.89937973e+00, ...,
             3.99323398e+04,  3.99919023e+04,  4.00514609e+04],
           [-1.12338089e+02, -5.27193794e+01,  6.89924192e+00, ...,
             3.99315039e+04,  3.99910703e+04,  4.00506250e+04],
           [-1.12335457e+02, -5.27181473e+01,  6.89908028e+00, ...,
             3.99305508e+04,  3.99901172e+04,  4.00496758e+04],
           ...,
           [-1.12328613e+02, -5.27149353e+01,  6.89866018e+00, ...,
             3.99280000e+04,  3.99875469e+04,  4.00471055e+04],
           [-1.12328613e+02, -5.27149353e+01,  6.89866018e+00, ...,
             3.99280000e+04,  3.99875469e+04,  4.00471055e+04],
           [-1.12328613e+02, -5.27149353e+01,  6.89866018e+00, ...,
             3.99280000e+04,  3.99875469e+04,  4.00471055e+04]], dtype=float32)

  • z
    (azimuth, range)
    float32
    -12.0 -6.0 ... 4.29e+03 4.296e+03

    array([[ -12.,   -6.,    0., ..., 4245., 4252., 4258.],
           [ -12.,   -6.,    0., ..., 4253., 4259., 4266.],
           [ -12.,   -6.,    0., ..., 4257., 4263., 4269.],
           ...,
           [ -12.,   -6.,    0., ..., 4283., 4290., 4296.],
           [ -12.,   -6.,    0., ..., 4283., 4290., 4296.],
           [ -12.,   -6.,    0., ..., 4283., 4290., 4296.]], dtype=float32)

  • lat
    (azimuth, range)
    float32
    38.9 38.9 38.9 ... 39.26 39.26

    array([[38.89737 , 38.897903, 38.89844 , ..., 39.2575  , 39.258034,
            39.25857 ],
           [38.89737 , 38.897903, 38.89844 , ..., 39.257492, 39.258022,
            39.258564],
           [38.89737 , 38.897903, 38.89844 , ..., 39.257484, 39.258015,
            39.258553],
           ...,
           [38.89737 , 38.897903, 38.89844 , ..., 39.257458, 39.258   ,
            39.258533],
           [38.89737 , 38.897903, 38.89844 , ..., 39.257458, 39.258   ,
            39.258533],
           [38.89737 , 38.897903, 38.89844 , ..., 39.257458, 39.258   ,
            39.258533]], dtype=float32)

  • lon
    (azimuth, range)
    float32
    -106.9 -106.9 ... -106.9 -106.9

    array([[-106.94321 , -106.94321 , -106.94321 , ..., -106.9422  ,
            -106.9422  , -106.94219 ],
           [-106.94321 , -106.94321 , -106.94321 , ..., -106.9422  ,
            -106.9422  , -106.94219 ],
           [-106.943214, -106.943214, -106.94321 , ..., -106.940605,
            -106.9406  , -106.9406  ],
           ...,
           [-106.943214, -106.94321 , -106.94321 , ..., -106.94154 ,
            -106.941536, -106.941536],
           [-106.943214, -106.94321 , -106.94321 , ..., -106.94154 ,
            -106.941536, -106.941536],
           [-106.943214, -106.94321 , -106.94321 , ..., -106.94154 ,
            -106.941536, -106.941536]], dtype=float32)

  • alt
    (azimuth, range)
    float64
    3.137e+03 3.143e+03 ... 7.445e+03

    array([[3137.19995117, 3143.19995117, 3149.19995117, ..., 7394.19995117,
            7401.19995117, 7407.19995117],
           [3137.19995117, 3143.19995117, 3149.19995117, ..., 7402.19995117,
            7408.19995117, 7415.19995117],
           [3137.19995117, 3143.19995117, 3149.19995117, ..., 7406.19995117,
            7412.19995117, 7418.19995117],
           ...,
           [3137.19995117, 3143.19995117, 3149.19995117, ..., 7432.19995117,
            7439.19995117, 7445.19995117],
           [3137.19995117, 3143.19995117, 3149.19995117, ..., 7432.19995117,
            7439.19995117, 7445.19995117],
           [3137.19995117, 3143.19995117, 3149.19995117, ..., 7432.19995117,
            7439.19995117, 7445.19995117]])

  • azimuth
    (azimuth)
    float32
    0.1251 0.1251 ... 0.2062 0.2062

    array([0.125068, 0.125068, 0.321548, ..., 0.206191, 0.206191, 0.206191],
          dtype=float32)

  • elevation
    (elevation)
    float32
    5.933 5.944 5.949 ... 5.988 5.988

    array([5.932617, 5.943604, 5.949097, ..., 5.987549, 5.987549, 5.987549],
          dtype=float32)

  • sweep
    (sweep)
    int32
    0

    array([0], dtype=int32)

  • range
    (range)
    float32
    -112.9 -53.0 ... 4.029e+04

    array([-1.129454e+02, -5.300440e+01,  6.936547e+00, ...,  4.016737e+04,
            4.022732e+04,  4.028725e+04], dtype=float32)

• Data variables: (0)
• Attributes: (0)

ds

<xarray.Dataset>
Dimensions:    (time: 8, azimuth: 1157, range: 675, elevation: 1157, sweep: 1)
Coordinates:
    x          (azimuth, range) float32 -0.2452 -0.1151 0.01506 ... 143.9 144.1
    y          (azimuth, range) float32 -112.3 -52.72 ... 3.999e+04 4.005e+04
    z          (azimuth, range) float32 -12.0 -6.0 0.0 ... 4.29e+03 4.296e+03
    lat        (azimuth, range) float32 38.9 38.9 38.9 ... 39.26 39.26 39.26
    lon        (azimuth, range) float32 -106.9 -106.9 -106.9 ... -106.9 -106.9
    alt        (azimuth, range) float64 3.137e+03 3.143e+03 ... 7.445e+03
  * azimuth    (azimuth) float32 0.1251 0.1251 0.3215 ... 0.2062 0.2062 0.2062
  * elevation  (elevation) float32 5.933 5.944 5.949 5.949 ... 5.988 5.988 5.988
  * sweep      (sweep) int32 0
  * range      (range) float32 -112.9 -53.0 6.937 ... 4.023e+04 4.029e+04
Dimensions without coordinates: time
Data variables:
    DBZ        (time, azimuth, range) float32 dask.array<chunksize=(1, 1157, 675), meta=np.ndarray>
    VEL        (time, azimuth, range) float32 dask.array<chunksize=(1, 1157, 675), meta=np.ndarray>
    WIDTH      (time, azimuth, range) float32 dask.array<chunksize=(1, 1157, 675), meta=np.ndarray>
    ZDR        (time, azimuth, range) float32 dask.array<chunksize=(1, 1157, 675), meta=np.ndarray>
    PHIDP      (time, azimuth, range) float32 dask.array<chunksize=(1, 1157, 675), meta=np.ndarray>
    RHOHV      (time, azimuth, range) float32 dask.array<chunksize=(1, 1157, 675), meta=np.ndarray>
    NCP        (time, azimuth, range) float32 dask.array<chunksize=(1, 1157, 675), meta=np.ndarray>
    SNR        (time, azimuth, range) float32 dask.array<chunksize=(1, 1157, 675), meta=np.ndarray>
    DBZhv      (time, azimuth, range) float32 dask.array<chunksize=(1, 1157, 675), meta=np.ndarray>

xarray.Dataset

• Dimensions:
  • time: 8
  • azimuth: 1157
  • range: 675
  • elevation: 1157
  • sweep: 1
• Coordinates: (10)
  • x
    (azimuth, range)
    float32
    -0.2452 -0.1151 ... 143.9 144.1

    array([[-2.4522302e-01, -1.1508123e-01,  1.5060368e-02, ...,
             8.7166641e+01,  8.7296654e+01,  8.7426666e+01],
           [-2.4521814e-01, -1.1507894e-01,  1.5060067e-02, ...,
             8.7164818e+01,  8.7294838e+01,  8.7424835e+01],
           [-6.3044101e-01, -2.9586101e-01,  3.8718525e-02, ...,
             2.2409538e+02,  2.2442966e+02,  2.2476392e+02],
           ...,
           [-4.0424091e-01, -1.8970707e-01,  2.4826448e-02, ...,
             1.4369028e+02,  1.4390459e+02,  1.4411891e+02],
           [-4.0424091e-01, -1.8970707e-01,  2.4826448e-02, ...,
             1.4369028e+02,  1.4390459e+02,  1.4411891e+02],
           [-4.0424091e-01, -1.8970707e-01,  2.4826448e-02, ...,
             1.4369028e+02,  1.4390459e+02,  1.4411891e+02]], dtype=float32)

  • y
    (azimuth, range)
    float32
    -112.3 -52.72 ... 4.005e+04

    array([[-1.12340324e+02, -5.27204285e+01,  6.89937973e+00, ...,
             3.99323398e+04,  3.99919023e+04,  4.00514609e+04],
           [-1.12338089e+02, -5.27193794e+01,  6.89924192e+00, ...,
             3.99315039e+04,  3.99910703e+04,  4.00506250e+04],
           [-1.12335457e+02, -5.27181473e+01,  6.89908028e+00, ...,
             3.99305508e+04,  3.99901172e+04,  4.00496758e+04],
           ...,
           [-1.12328613e+02, -5.27149353e+01,  6.89866018e+00, ...,
             3.99280000e+04,  3.99875469e+04,  4.00471055e+04],
           [-1.12328613e+02, -5.27149353e+01,  6.89866018e+00, ...,
             3.99280000e+04,  3.99875469e+04,  4.00471055e+04],
           [-1.12328613e+02, -5.27149353e+01,  6.89866018e+00, ...,
             3.99280000e+04,  3.99875469e+04,  4.00471055e+04]], dtype=float32)

  • z
    (azimuth, range)
    float32
    -12.0 -6.0 ... 4.29e+03 4.296e+03

    array([[ -12.,   -6.,    0., ..., 4245., 4252., 4258.],
           [ -12.,   -6.,    0., ..., 4253., 4259., 4266.],
           [ -12.,   -6.,    0., ..., 4257., 4263., 4269.],
           ...,
           [ -12.,   -6.,    0., ..., 4283., 4290., 4296.],
           [ -12.,   -6.,    0., ..., 4283., 4290., 4296.],
           [ -12.,   -6.,    0., ..., 4283., 4290., 4296.]], dtype=float32)

  • lat
    (azimuth, range)
    float32
    38.9 38.9 38.9 ... 39.26 39.26

    array([[38.89737 , 38.897903, 38.89844 , ..., 39.2575  , 39.258034,
            39.25857 ],
           [38.89737 , 38.897903, 38.89844 , ..., 39.257492, 39.258022,
            39.258564],
           [38.89737 , 38.897903, 38.89844 , ..., 39.257484, 39.258015,
            39.258553],
           ...,
           [38.89737 , 38.897903, 38.89844 , ..., 39.257458, 39.258   ,
            39.258533],
           [38.89737 , 38.897903, 38.89844 , ..., 39.257458, 39.258   ,
            39.258533],
           [38.89737 , 38.897903, 38.89844 , ..., 39.257458, 39.258   ,
            39.258533]], dtype=float32)

  • lon
    (azimuth, range)
    float32
    -106.9 -106.9 ... -106.9 -106.9

    array([[-106.94321 , -106.94321 , -106.94321 , ..., -106.9422  ,
            -106.9422  , -106.94219 ],
           [-106.94321 , -106.94321 , -106.94321 , ..., -106.9422  ,
            -106.9422  , -106.94219 ],
           [-106.943214, -106.943214, -106.94321 , ..., -106.940605,
            -106.9406  , -106.9406  ],
           ...,
           [-106.943214, -106.94321 , -106.94321 , ..., -106.94154 ,
            -106.941536, -106.941536],
           [-106.943214, -106.94321 , -106.94321 , ..., -106.94154 ,
            -106.941536, -106.941536],
           [-106.943214, -106.94321 , -106.94321 , ..., -106.94154 ,
            -106.941536, -106.941536]], dtype=float32)

  • alt
    (azimuth, range)
    float64
    3.137e+03 3.143e+03 ... 7.445e+03

    array([[3137.19995117, 3143.19995117, 3149.19995117, ..., 7394.19995117,
            7401.19995117, 7407.19995117],
           [3137.19995117, 3143.19995117, 3149.19995117, ..., 7402.19995117,
            7408.19995117, 7415.19995117],
           [3137.19995117, 3143.19995117, 3149.19995117, ..., 7406.19995117,
            7412.19995117, 7418.19995117],
           ...,
           [3137.19995117, 3143.19995117, 3149.19995117, ..., 7432.19995117,
            7439.19995117, 7445.19995117],
           [3137.19995117, 3143.19995117, 3149.19995117, ..., 7432.19995117,
            7439.19995117, 7445.19995117],
           [3137.19995117, 3143.19995117, 3149.19995117, ..., 7432.19995117,
            7439.19995117, 7445.19995117]])

  • azimuth
    (azimuth)
    float32
    0.1251 0.1251 ... 0.2062 0.2062

    array([0.125068, 0.125068, 0.321548, ..., 0.206191, 0.206191, 0.206191],
          dtype=float32)

  • elevation
    (elevation)
    float32
    5.933 5.944 5.949 ... 5.988 5.988

    array([5.932617, 5.943604, 5.949097, ..., 5.987549, 5.987549, 5.987549],
          dtype=float32)

  • sweep
    (sweep)
    int32
    0

    array([0], dtype=int32)

  • range
    (range)
    float32
    -112.9 -53.0 ... 4.029e+04

    array([-1.129454e+02, -5.300440e+01,  6.936547e+00, ...,  4.016737e+04,
            4.022732e+04,  4.028725e+04], dtype=float32)

• Data variables: (9)
  • DBZ
    (time, azimuth, range)
    float32
    dask.array<chunksize=(1, 1157, 675), meta=np.ndarray>

    Array Chunk Bytes 23.83 MiB 2.98 MiB Shape (8, 1157, 675) (1, 1157, 675) Count 10 Tasks 8 Chunks Type float32 numpy.ndarray

  • VEL
    (time, azimuth, range)
    float32
    dask.array<chunksize=(1, 1157, 675), meta=np.ndarray>

    Array Chunk Bytes 23.83 MiB 2.98 MiB Shape (8, 1157, 675) (1, 1157, 675) Count 10 Tasks 8 Chunks Type float32 numpy.ndarray

  • WIDTH
    (time, azimuth, range)
    float32
    dask.array<chunksize=(1, 1157, 675), meta=np.ndarray>

    Array Chunk Bytes 23.83 MiB 2.98 MiB Shape (8, 1157, 675) (1, 1157, 675) Count 10 Tasks 8 Chunks Type float32 numpy.ndarray

  • ZDR
    (time, azimuth, range)
    float32
    dask.array<chunksize=(1, 1157, 675), meta=np.ndarray>

    Array Chunk Bytes 23.83 MiB 2.98 MiB Shape (8, 1157, 675) (1, 1157, 675) Count 10 Tasks 8 Chunks Type float32 numpy.ndarray

  • PHIDP
    (time, azimuth, range)
    float32
    dask.array<chunksize=(1, 1157, 675), meta=np.ndarray>

    Array Chunk Bytes 23.83 MiB 2.98 MiB Shape (8, 1157, 675) (1, 1157, 675) Count 10 Tasks 8 Chunks Type float32 numpy.ndarray

  • RHOHV
    (time, azimuth, range)
    float32
    dask.array<chunksize=(1, 1157, 675), meta=np.ndarray>

    Array Chunk Bytes 23.83 MiB 2.98 MiB Shape (8, 1157, 675) (1, 1157, 675) Count 10 Tasks 8 Chunks Type float32 numpy.ndarray

  • NCP
    (time, azimuth, range)
    float32
    dask.array<chunksize=(1, 1157, 675), meta=np.ndarray>

    Array Chunk Bytes 23.83 MiB 2.98 MiB Shape (8, 1157, 675) (1, 1157, 675) Count 10 Tasks 8 Chunks Type float32 numpy.ndarray

  • SNR
    (time, azimuth, range)
    float32
    dask.array<chunksize=(1, 1157, 675), meta=np.ndarray>

    Array Chunk Bytes 23.83 MiB 2.98 MiB Shape (8, 1157, 675) (1, 1157, 675) Count 10 Tasks 8 Chunks Type float32 numpy.ndarray

  • DBZhv
    (time, azimuth, range)
    float32
    dask.array<chunksize=(1, 1157, 675), meta=np.ndarray>

    Array Chunk Bytes 23.83 MiB 2.98 MiB Shape (8, 1157, 675) (1, 1157, 675) Count 10 Tasks 8 Chunks Type float32 numpy.ndarray

• Attributes: (0)

ds = ds.drop(['elevation', 'sweep'])

ds

<xarray.Dataset>
Dimensions:  (time: 8, azimuth: 1157, range: 675)
Coordinates:
    x        (azimuth, range) float32 -0.2452 -0.1151 0.01506 ... 143.9 144.1
    y        (azimuth, range) float32 -112.3 -52.72 ... 3.999e+04 4.005e+04
    z        (azimuth, range) float32 -12.0 -6.0 0.0 ... 4.29e+03 4.296e+03
    lat      (azimuth, range) float32 38.9 38.9 38.9 38.9 ... 39.26 39.26 39.26
    lon      (azimuth, range) float32 -106.9 -106.9 -106.9 ... -106.9 -106.9
    alt      (azimuth, range) float64 3.137e+03 3.143e+03 ... 7.445e+03
  * azimuth  (azimuth) float32 0.1251 0.1251 0.3215 ... 0.2062 0.2062 0.2062
  * range    (range) float32 -112.9 -53.0 6.937 ... 4.023e+04 4.029e+04
Dimensions without coordinates: time
Data variables:
    DBZ      (time, azimuth, range) float32 dask.array<chunksize=(1, 1157, 675), meta=np.ndarray>
    VEL      (time, azimuth, range) float32 dask.array<chunksize=(1, 1157, 675), meta=np.ndarray>
    WIDTH    (time, azimuth, range) float32 dask.array<chunksize=(1, 1157, 675), meta=np.ndarray>
    ZDR      (time, azimuth, range) float32 dask.array<chunksize=(1, 1157, 675), meta=np.ndarray>
    PHIDP    (time, azimuth, range) float32 dask.array<chunksize=(1, 1157, 675), meta=np.ndarray>
    RHOHV    (time, azimuth, range) float32 dask.array<chunksize=(1, 1157, 675), meta=np.ndarray>
    NCP      (time, azimuth, range) float32 dask.array<chunksize=(1, 1157, 675), meta=np.ndarray>
    SNR      (time, azimuth, range) float32 dask.array<chunksize=(1, 1157, 675), meta=np.ndarray>
    DBZhv    (time, azimuth, range) float32 dask.array<chunksize=(1, 1157, 675), meta=np.ndarray>

xarray.Dataset

• Dimensions:
  • time: 8
  • azimuth: 1157
  • range: 675
• Coordinates: (8)
  • x
    (azimuth, range)
    float32
    -0.2452 -0.1151 ... 143.9 144.1

    array([[-2.4522302e-01, -1.1508123e-01,  1.5060368e-02, ...,
             8.7166641e+01,  8.7296654e+01,  8.7426666e+01],
           [-2.4521814e-01, -1.1507894e-01,  1.5060067e-02, ...,
             8.7164818e+01,  8.7294838e+01,  8.7424835e+01],
           [-6.3044101e-01, -2.9586101e-01,  3.8718525e-02, ...,
             2.2409538e+02,  2.2442966e+02,  2.2476392e+02],
           ...,
           [-4.0424091e-01, -1.8970707e-01,  2.4826448e-02, ...,
             1.4369028e+02,  1.4390459e+02,  1.4411891e+02],
           [-4.0424091e-01, -1.8970707e-01,  2.4826448e-02, ...,
             1.4369028e+02,  1.4390459e+02,  1.4411891e+02],
           [-4.0424091e-01, -1.8970707e-01,  2.4826448e-02, ...,
             1.4369028e+02,  1.4390459e+02,  1.4411891e+02]], dtype=float32)

  • y
    (azimuth, range)
    float32
    -112.3 -52.72 ... 4.005e+04

    array([[-1.12340324e+02, -5.27204285e+01,  6.89937973e+00, ...,
             3.99323398e+04,  3.99919023e+04,  4.00514609e+04],
           [-1.12338089e+02, -5.27193794e+01,  6.89924192e+00, ...,
             3.99315039e+04,  3.99910703e+04,  4.00506250e+04],
           [-1.12335457e+02, -5.27181473e+01,  6.89908028e+00, ...,
             3.99305508e+04,  3.99901172e+04,  4.00496758e+04],
           ...,
           [-1.12328613e+02, -5.27149353e+01,  6.89866018e+00, ...,
             3.99280000e+04,  3.99875469e+04,  4.00471055e+04],
           [-1.12328613e+02, -5.27149353e+01,  6.89866018e+00, ...,
             3.99280000e+04,  3.99875469e+04,  4.00471055e+04],
           [-1.12328613e+02, -5.27149353e+01,  6.89866018e+00, ...,
             3.99280000e+04,  3.99875469e+04,  4.00471055e+04]], dtype=float32)

  • z
    (azimuth, range)
    float32
    -12.0 -6.0 ... 4.29e+03 4.296e+03

    array([[ -12.,   -6.,    0., ..., 4245., 4252., 4258.],
           [ -12.,   -6.,    0., ..., 4253., 4259., 4266.],
           [ -12.,   -6.,    0., ..., 4257., 4263., 4269.],
           ...,
           [ -12.,   -6.,    0., ..., 4283., 4290., 4296.],
           [ -12.,   -6.,    0., ..., 4283., 4290., 4296.],
           [ -12.,   -6.,    0., ..., 4283., 4290., 4296.]], dtype=float32)

  • lat
    (azimuth, range)
    float32
    38.9 38.9 38.9 ... 39.26 39.26

    array([[38.89737 , 38.897903, 38.89844 , ..., 39.2575  , 39.258034,
            39.25857 ],
           [38.89737 , 38.897903, 38.89844 , ..., 39.257492, 39.258022,
            39.258564],
           [38.89737 , 38.897903, 38.89844 , ..., 39.257484, 39.258015,
            39.258553],
           ...,
           [38.89737 , 38.897903, 38.89844 , ..., 39.257458, 39.258   ,
            39.258533],
           [38.89737 , 38.897903, 38.89844 , ..., 39.257458, 39.258   ,
            39.258533],
           [38.89737 , 38.897903, 38.89844 , ..., 39.257458, 39.258   ,
            39.258533]], dtype=float32)

  • lon
    (azimuth, range)
    float32
    -106.9 -106.9 ... -106.9 -106.9

    array([[-106.94321 , -106.94321 , -106.94321 , ..., -106.9422  ,
            -106.9422  , -106.94219 ],
           [-106.94321 , -106.94321 , -106.94321 , ..., -106.9422  ,
            -106.9422  , -106.94219 ],
           [-106.943214, -106.943214, -106.94321 , ..., -106.940605,
            -106.9406  , -106.9406  ],
           ...,
           [-106.943214, -106.94321 , -106.94321 , ..., -106.94154 ,
            -106.941536, -106.941536],
           [-106.943214, -106.94321 , -106.94321 , ..., -106.94154 ,
            -106.941536, -106.941536],
           [-106.943214, -106.94321 , -106.94321 , ..., -106.94154 ,
            -106.941536, -106.941536]], dtype=float32)

  • alt
    (azimuth, range)
    float64
    3.137e+03 3.143e+03 ... 7.445e+03

    array([[3137.19995117, 3143.19995117, 3149.19995117, ..., 7394.19995117,
            7401.19995117, 7407.19995117],
           [3137.19995117, 3143.19995117, 3149.19995117, ..., 7402.19995117,
            7408.19995117, 7415.19995117],
           [3137.19995117, 3143.19995117, 3149.19995117, ..., 7406.19995117,
            7412.19995117, 7418.19995117],
           ...,
           [3137.19995117, 3143.19995117, 3149.19995117, ..., 7432.19995117,
            7439.19995117, 7445.19995117],
           [3137.19995117, 3143.19995117, 3149.19995117, ..., 7432.19995117,
            7439.19995117, 7445.19995117],
           [3137.19995117, 3143.19995117, 3149.19995117, ..., 7432.19995117,
            7439.19995117, 7445.19995117]])

  • azimuth
    (azimuth)
    float32
    0.1251 0.1251 ... 0.2062 0.2062

    array([0.125068, 0.125068, 0.321548, ..., 0.206191, 0.206191, 0.206191],
          dtype=float32)

  • range
    (range)
    float32
    -112.9 -53.0 ... 4.029e+04

    array([-1.129454e+02, -5.300440e+01,  6.936547e+00, ...,  4.016737e+04,
            4.022732e+04,  4.028725e+04], dtype=float32)

• Data variables: (9)
  • DBZ
    (time, azimuth, range)
    float32
    dask.array<chunksize=(1, 1157, 675), meta=np.ndarray>

    Array Chunk Bytes 23.83 MiB 2.98 MiB Shape (8, 1157, 675) (1, 1157, 675) Count 10 Tasks 8 Chunks Type float32 numpy.ndarray

  • VEL
    (time, azimuth, range)
    float32
    dask.array<chunksize=(1, 1157, 675), meta=np.ndarray>

    Array Chunk Bytes 23.83 MiB 2.98 MiB Shape (8, 1157, 675) (1, 1157, 675) Count 10 Tasks 8 Chunks Type float32 numpy.ndarray

  • WIDTH
    (time, azimuth, range)
    float32
    dask.array<chunksize=(1, 1157, 675), meta=np.ndarray>

    Array Chunk Bytes 23.83 MiB 2.98 MiB Shape (8, 1157, 675) (1, 1157, 675) Count 10 Tasks 8 Chunks Type float32 numpy.ndarray

  • ZDR
    (time, azimuth, range)
    float32
    dask.array<chunksize=(1, 1157, 675), meta=np.ndarray>

    Array Chunk Bytes 23.83 MiB 2.98 MiB Shape (8, 1157, 675) (1, 1157, 675) Count 10 Tasks 8 Chunks Type float32 numpy.ndarray

  • PHIDP
    (time, azimuth, range)
    float32
    dask.array<chunksize=(1, 1157, 675), meta=np.ndarray>

    Array Chunk Bytes 23.83 MiB 2.98 MiB Shape (8, 1157, 675) (1, 1157, 675) Count 10 Tasks 8 Chunks Type float32 numpy.ndarray

  • RHOHV
    (time, azimuth, range)
    float32
    dask.array<chunksize=(1, 1157, 675), meta=np.ndarray>

    Array Chunk Bytes 23.83 MiB 2.98 MiB Shape (8, 1157, 675) (1, 1157, 675) Count 10 Tasks 8 Chunks Type float32 numpy.ndarray

  • NCP
    (time, azimuth, range)
    float32
    dask.array<chunksize=(1, 1157, 675), meta=np.ndarray>

    Array Chunk Bytes 23.83 MiB 2.98 MiB Shape (8, 1157, 675) (1, 1157, 675) Count 10 Tasks 8 Chunks Type float32 numpy.ndarray

  • SNR
    (time, azimuth, range)
    float32
    dask.array<chunksize=(1, 1157, 675), meta=np.ndarray>

    Array Chunk Bytes 23.83 MiB 2.98 MiB Shape (8, 1157, 675) (1, 1157, 675) Count 10 Tasks 8 Chunks Type float32 numpy.ndarray

  • DBZhv
    (time, azimuth, range)
    float32
    dask.array<chunksize=(1, 1157, 675), meta=np.ndarray>

    Array Chunk Bytes 23.83 MiB 2.98 MiB Shape (8, 1157, 675) (1, 1157, 675) Count 10 Tasks 8 Chunks Type float32 numpy.ndarray

• Attributes: (0)

dbz_hist = ds.where(ds.ZDR > -1000).DBZ.hvplot.hist(bins=np.arange(-20, 40, 1), title='DBZ Values')
dbz_hist

ncp_hist = ds.where(ds.ZDR > -1000).NCP.hvplot.hist(bins=np.arange(0, 1.01, 0.01), title='NCP Values')
ncp_hist

zdr_hist = ds.where(ds.ZDR > -1000).ZDR.hvplot.hist(bins=np.arange(-10, 10.1, .1), title='ZDR Values')
zdr_hist

zdr_hist

(dbz_hist + ncp_hist + zdr_hist).cols(1)

ds = ds.where((ds.DBZ > -30) & 
              (ds.ZDR > -20))

ds.isel(time=0).compute().hvplot.quadmesh(x='lon',
                                          y='lat',
                                          z=['DBZ', 'VEL', 'ZDR'],
                                          rasterize=True,
                                          width=600,
                                          height=400,
                                          geo=True,
                                          alpha=0.1,
                                          selection_color={'NaN': (0, 0, 0, 0)},
                                          #projection=ccrs.PlateCarree(),
                                          cmap='pyart_Carbone42',
                                          features={'rivers':'110m'},
                                          tiles='StamenTerrainRetina').opts("Image", alpha=0.6)

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Interactive Plot of Ka-Band Data

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X-Band RHI